Fraud is getting faster. Instant payments compress the recovery window, while AI makes impersonation and business-email-compromise attacks more convincing and scalable. Dream Hannah adds a continuous reconciliation layer that checks what actually moved against what was supposed to happen—so suspicious breaks surface while there is still time to act.
Payment fraud is already widespread. The shift to faster, more final payment methods raises the cost of late detection, while AI-enabled impersonation makes it harder to rely on appearance, voice, email or a seemingly legitimate payment request alone.
Sources: Association for Financial Professionals, 2026 Payments Fraud and Control Survey; FBI Internet Crime Complaint Center, 2025 Annual Report. Statistics are reported figures and do not represent Dream Hannah customer outcomes.
Speed is a feature—until a fraudulent payment gets through. The Federal Reserve notes that instant payments are generally final and irrevocable, creating additional fraud-prevention and detection challenges and increasing the risk of irreversible fraudulent debits.
Federal Reserve analysis ↗AFP’s 2026 fraud research specifically highlights growing concern about AI-enabled fraud and deepfake voice and video used to impersonate executives, vendors and other trusted parties.
AFP 2026 Survey ↗Business email compromise remains one of the most common payment-fraud patterns. A request can pass familiar controls because the attacker is imitating a real executive, vendor or workflow—not obviously presenting as fraud.
FBI IC3 2025 ↗Dream Hannah complements—not replaces—authorization, identity verification, approvals, bank controls and fraud scoring. Those controls guard the front door. Hannah continuously compares the payment that actually moved to the business evidence that should explain it.
Necessary controls attempt to decide whether a transaction or instruction is legitimate before release.
Dream Hannah checks whether the actual movement agrees with the underlying obligation, beneficiary, source record and bank outcome.
The engine combines deterministic matching, AI analysis and human learning across payment and operational data. The goal is not to “guess fraud.” It is to identify when the money and the underlying business truth no longer agree.
Bank feeds, payment-network records, remittances, ledgers, leases, policies, claims and source-system extracts create the full picture.
Exact and tolerance rules clear the obvious cases first: amount, date, reference, beneficiary and expected-vs-actual checks.
AI works the ambiguous tail—missing references, name variants, combined payments, unusual context and conflicting evidence.
Unmatched or suspicious items become a prioritized queue, with the reason and evidence attached rather than a raw list of breaks.
Material or uncertain cases route to people for judgment. Reviewer decisions teach the engine and preserve institutional knowledge.
Every decision keeps its lineage—what was expected, what moved, what differed, why it was flagged and how the case was resolved.
| Signal | What Hannah compares | What it may indicate | Typical priority |
|---|---|---|---|
| Changed beneficiary details | Vendor / claimant profile vs. payment destination | Account-change fraud, impersonation or process break | High |
| Potential duplicate | Source obligation vs. multiple payments / bank debits | Duplicate invoice, claim, refund or reissue | High |
| Amount variance | Expected obligation vs. actual amount | Manipulated amount, overpay, short-pay or fee/commission variance | Review |
| Missing receipt / deposit | Expected incoming payment vs. bank cash | Diversion, failed payment, misapplication or timing break | High |
| Unmatched cash | Bank deposit vs. lease, policy, invoice or remittance | Misapplied funds, missing records or unexplained activity | Review |
| Unexpected payment state | Approved instruction vs. issued / cleared / returned status | Failed, reissued, orphaned or irregular payout | Review |
Dream Hannah turns an anomaly into a structured investigation with evidence. Recovery or payment action still depends on the payment rail, bank, timing and your authorized team—but the case starts sooner and with less manual reconstruction.
Detect the mismatch continuously instead of waiting for close or audit.
Show the source record, amount, beneficiary, timing and conflicting evidence.
Prioritize the exception and place it with the finance, treasury or payment-operations team.
Give the team the evidence needed to investigate, contact counterparties or banks, and take available recovery steps.
Record the outcome so the same pattern becomes easier to identify the next time.
Premium in. Claims out. Multiple systems and counterparties create gaps fraud and error can hide inside.
Rent, deposits, vendor payments, owner distributions and trust accounts move money in every direction.
These are the principal public sources used for the fraud-risk statements on this page.
Reports 76% of organizations experienced attempted or actual payments fraud in 2025 and discusses BEC, AI-enabled fraud, deepfakes, controls and recovery.
Reports 74% BEC exposure in 2025, 17% use of AI to combat payments fraud, and treasury’s role in detection, reporting and recovery.
Reports $20.877 billion in total complaint losses, including $3.046 billion attributed to business email compromise; the report also identifies AI-related complaints as an emerging descriptor.
Explains that instant payments are generally final and irrevocable and can introduce additional fraud-prevention and detection challenges.
Public-source statistics describe industry conditions, not Dream Hannah performance. Product capability descriptions are based on Dream Payments / Brisc AI launch and reconciliation materials. Accessed August 9, 2026.
Start with a reconciliation and fraud assessment. See where payments stop agreeing with the records behind them—and how quickly Dream Hannah can surface the exceptions.